Abstract/Description

This article describes a new tool for visualising genetic algorithms, (GAs) which is designed in order to allow the implicit mechanisms of the GA | i.e. crossover and mutation | to be thoroughly analysed. This allows the user to determine whether these mechanisms are essential to a GAs performance, and if so, to provide a principled means of setting the parameters associated with them, based on a sound understanding of their eects. The use of the tool is illustrated by applying to the analysis of a jobshop scheduling problem, in order to choose eective operators, and to determine appropriate settings for them. We show that by analysing two crossover operators and a mutation operator, we can re ne the choice and settings of these parameters in order to improve the performance of the GA on the particular problem chosen. When the new operators are applied to a wider range of problems of the same type, a similar improvement in performance is observed.